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HunyuanVideo 1.5 (#10819)

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* init

* update

* Update model.py

* Update model.py

* remove print

* Fix text encoding

* Prevent empty negative prompt

Really doesn't work otherwise

* fp16 works

* I2V

* Update model_base.py

* Update nodes_hunyuan.py

* Better latent rgb factors

* Use the correct sigclip output...

* Support HunyuanVideo1.5 SR model

* whitespaces...

* Proper latent channel count

* SR model fixes

This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already

* vae_refiner: roll the convolution through temporal

Work in progress.

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

* Support HunyuanVideo15 latent resampler

* fix

* Some cleanup

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Proper hyvid15 I2V channels

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Fix TokenRefiner for fp16

Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.

* Bugfix for the HunyuanVideo15 SR model

* vae_refiner: roll the convolution through temporal II

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).

Fixed names, cleaned up code.

* Allow any number of input frames in VAE.

* Better VAE encode mem estimation.

* Lowvram fix.

* Fix hunyuan image 2.1 refiner.

* Fix mistake.

* Name changes.

* Rename.

* Whitespace.

* Fix.

* Fix.

---------

Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>
This commit is contained in:
comfyanonymous
2025-11-20 19:44:43 -08:00
committed by GitHub
parent 10e90a5757
commit 943b3b615d
15 changed files with 777 additions and 126 deletions
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60
comfy/latent_formats.py
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@@ -611,6 +611,66 @@ class HunyuanImage21Refiner(LatentFormat):
latent_dimensions = 3
scale_factor = 1.03682
def process_in(self, latent):
out = latent * self.scale_factor
out = torch.cat((out[:, :, :1], out), dim=2)
out = out.permute(0, 2, 1, 3, 4)
b, f_times_2, c, h, w = out.shape
out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
out = out.permute(0, 2, 1, 3, 4).contiguous()
return out
def process_out(self, latent):
z = latent / self.scale_factor
z = z.permute(0, 2, 1, 3, 4)
b, f, c, h, w = z.shape
z = z.reshape(b, f, 2, c // 2, h, w)
z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
z = z.permute(0, 2, 1, 3, 4)
z = z[:, :, 1:]
return z
class HunyuanVideo15(LatentFormat):
latent_rgb_factors = [
[ 0.0568, -0.0521, -0.0131],
[ 0.0014, 0.0735, 0.0326],
[ 0.0186, 0.0531, -0.0138],
[-0.0031, 0.0051, 0.0288],
[ 0.0110, 0.0556, 0.0432],
[-0.0041, -0.0023, -0.0485],
[ 0.0530, 0.0413, 0.0253],
[ 0.0283, 0.0251, 0.0339],
[ 0.0277, -0.0372, -0.0093],
[ 0.0393, 0.0944, 0.1131],
[ 0.0020, 0.0251, 0.0037],
[-0.0017, 0.0012, 0.0234],
[ 0.0468, 0.0436, 0.0203],
[ 0.0354, 0.0439, -0.0233],
[ 0.0090, 0.0123, 0.0346],
[ 0.0382, 0.0029, 0.0217],
[ 0.0261, -0.0300, 0.0030],
[-0.0088, -0.0220, -0.0283],
[-0.0272, -0.0121, -0.0363],
[-0.0664, -0.0622, 0.0144],
[ 0.0414, 0.0479, 0.0529],
[ 0.0355, 0.0612, -0.0247],
[ 0.0147, 0.0264, 0.0174],
[ 0.0438, 0.0038, 0.0542],
[ 0.0431, -0.0573, -0.0033],
[-0.0162, -0.0211, -0.0406],
[-0.0487, -0.0295, -0.0393],
[ 0.0005, -0.0109, 0.0253],
[ 0.0296, 0.0591, 0.0353],
[ 0.0119, 0.0181, -0.0306],
[-0.0085, -0.0362, 0.0229],
[ 0.0005, -0.0106, 0.0242]
]
latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
latent_channels = 32
latent_dimensions = 3
scale_factor = 1.03682
class Hunyuan3Dv2(LatentFormat):
latent_channels = 64
latent_dimensions = 1